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Progress in Artificial Intelligence

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08.09.2018 | Regular Paper

SocialLink: exploiting graph embeddings to link DBpedia entities to Twitter profiles

verfasst von: Yaroslav Nechaev, Francesco Corcoglioniti, Claudio Giuliano

Erschienen in: Progress in Artificial Intelligence | Ausgabe 4/2018

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Abstract

SocialLink is a project designed to match social media profiles on Twitter to corresponding entities in DBpedia. Built to bridge the vibrant Twitter social media world and the Linked Open Data cloud, SocialLink enables knowledge transfer between the two, both assisting Semantic Web practitioners in better harvesting the vast amounts of information available on Twitter and allowing leveraging of DBpedia data for social media analysis tasks. In this paper, we further extend the original SocialLink approach by exploiting graph-based features based on both DBpedia and Twitter, represented as graph embeddings learned from vast amounts of unlabeled data. The introduction of such new features required to redesign our deep neural network-based candidate selection algorithm and, as a result, we experimentally demonstrate a significant improvement of the performances of SocialLink.

Vorheriger Artikel Applied cognitive computing: challenges, approaches, and real-world experiences

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http://sociallink.futuro.media

https://meta.wikimedia.org/wiki/Grants:Project/Hjfocs/soweego

https://github.com/Remper/sociallink

https://zenodo.org/record/820160

We start from KB entries as they are entirely known in advance, differently from social network profiles that can be only queried or (partially) acquired via expensive crawling.

English DBpedia version 2016–04, for what concerns the experiments reported in this paper (to enable comparison with original approach in [25]). The SocialLink LOD dataset released online is instead built using data from all language chapters of the most recent DBpedia.

Entity alive status is gathered from temporal properties like dbo:deathDate, dbo:deathYear, dbo:closingYear, dbo:closed, dbo:extinctionYear, dbo:extinctionDate, wikidata:P570, wikidata:P20, wikidata:P509, or properties implying death like dbo:deathPlace, dbo:deathCause, dbo:causeOfDeath.

Gold alignments derive from selected foaf:isPrimaryTopicOf and wikidata:P2002 triples of entities assumed living.

http://flink.apache.org/

See [1, Section 3.2] for a detailed description of how LSA embeddings are computed.

PageRank Split embeddings downloaded from http://data.dws.informatik.uni-mannheim.de/rdf2vec/models/DBpedia/2016--04/GlobalVectors/

The regression models described in this section perform an approximate matrix factorization rather than the exact one used, for example, by LSA.

We compare the performances of the subset with the ones of its complement using the non-paired approximate randomization test.

http://datahub.io/dataset/sociallink

http://github.com/Remper/sociallink

http://sociallink.futuro.media/sparql

http://nlp.stanford.edu/data/glove.42B.300d.zip

https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md

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Titel: SocialLink: exploiting graph embeddings to link DBpedia entities to Twitter profiles
verfasst von: Yaroslav Nechaev
Francesco Corcoglioniti
Claudio Giuliano
Publikationsdatum: 08.09.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: Progress in Artificial Intelligence / Ausgabe 4/2018
Print ISSN: 2192-6352
Elektronische ISSN: 2192-6360
DOI: https://doi.org/10.1007/s13748-018-0160-x

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